3.3.3. Qualitative Assessment of Clusters

A comparison of all the measurement points for each parameter in the database would lead to the analysis of the changeability of 48 parameters for each of the six clusters. Therefore, the authors suggest only analyzing those PQ parameters that were indicated as important with regards to the obtained classification (according to the predictor importance rate).

Table 2 contains the comparison of the selected PQ parameters for each cluster in terms of the mean, minimal, maximal, and standard deviation values.


**Table 2.** Comparison of the PQ level for different clusters.


**Table 2.** *Cont.*

where:

minimal—the minimal value of the parameter that may be found for the observed cluster maximal—the maximal value of the parameter that may be found for the observed cluster mean—the mean value calculated from all the data for the observed cluster standard deviation—the standard deviation calculated from all the data for the observed cluster.

A comparison of the level of the PQ parameters for different clusters is equivalent to the comparison of the different working conditions of an electrical power network. The examples of such a comparison may be as follows:


The presented examples about the comparison of the level of the PQ parameters for different clusters assure simplified information concerning the differences between working conditions. However, the working condition for defining the cluster c3 is unknown, but due to the indicated analysis, it is possible to define that during this time there was a higher than normal level of harmonics for T3 and WM. Thanks to this, attention could be paid to this time in order to find the reason for such high harmonic content and to reduce it in the future. Additionally, after automatic classification of the data, it is possible to show the impact of DG on the level of power quality in the electrical power network of the mining industry.
